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논문 하나는 아직 읽는중이라, 정리되는대로 수정해서 올리겠습니다..! 미리 reflection 제출합니다.

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## Book and paper reading

1. New concepts you have learned this week?

2. Interpret beta-coefficient in linear regression

3. Interpret beta-coefficient (odds ratio) in logistic regression

4. What is the difference between interventional study and observational study?

5. What is the difference between case-control study and cohort study?

6. What is the difference between odds ratio and relative risk?

7. Find 2 papers related to your research topic and summary important findings

## Analysis result: perform the analysis for your own research topic, using data you have prepared in Week 2

1. Import relative abundance data (data.phylum3 to data.species3) and clinical combined data

2. Perform linear regression between your selected factor and alpha-diversity index, interpretation

3. Perform logistic regression between your selected factor and F/B ratio, interpretation

4. Perform LEfSe analysis, interpretation

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[Week 3] Descriptive statistic and visualization

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[Week 3] Descriptive statistic and visualization

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## Book and paper reading

1. New concepts you have learned this week?

2. Find 2 papers related to your research topic and summary important findings

## Analysis result: perform the analysis for your own research topic, using data you have prepared in Week 2

1. Import data.phym2 è data.genus2, and combined clinical data

2. Provide table of general characteristics of study participants according to diabetes (민규), hypertension (하림), and obesity (지민) statuses (refer to part 4), and interpretation

3. Provide bar plot figures of phylum, class, order, family, and genus abundance in total 331 patients (refer to part 5)

4. Provide bar plot figures of abundance data for each group of diabetes (민규), hypertension (하림), and obesity (지민) (refer to part 5)

5. Provide bar plot figures of 3 alpha-diversity indices (refer to part 5)

6. Perform Wilcoxon test for difference of 244 taxonomy (phylum to genus) and 3 alpha-diversity indices between groups of diabetes (민규), hypertension (하림), and obesity (지민) (refer to part 6). Which microbiome features are difference between 2 groups?

7. Provide the code you use for the analysis

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[Week 2] Microbiome Data Preparation: Presentation file, data file, and assignments

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[Week 2] Microbiome Data Preparation: Presentation file, data file, and assignments

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Book and paper reading

1. New concepts you have learned this week?

2. What is the difference between Chao1, Shannon, Simpson index (Microbiome book, chapter 6)

3. Summary important findings of the following papers:

- [Obesity] The gut microbiome profile in obesity: a systematic review. 2018. International Journal of Endocrinology

- [Hypertension] Gut microbiota dysbiosis contributes to the development of hypertension. 2017. Microbiome.

- [Antidiabetic drugs] Bi-directional drug-microbiome interactions of anti-diabetics. 2020. EBioMedicine.

Microbiome data

1. Import ”MicrobiomeData.csv” file into R (read.csv R function)

2. Generate relative abundance data (data.phylum3, data.class3, data.order3, data.family3, data.genus3, data.species3)

3. Export these relative abundance data as csv files (write.csv R function) ==> submit

4. Calculate alpha diversity, output is ‘alpha.df’

Clinical data

1. Import “ClinicalData.csv” file into R (read.csv R function)

2. Create new variable, named “r.age”, with the following values: 0 (age <=50), 1 (age<=60), 2 (age<=70), 3 (age>70) (ifelse R function)

3. Create new variable, named “BMI”, which is defined as weight/height^2 (kg/m2)

4. Create new variable, named “r.bmi”, with the following values: 0 (BMI <=24.9), 1 (BMI>25.0) (ifelse R function)

5. Create new variable, named “ptid” = “X” + value of ”No” column (ex. No=232 ==> ptid=X232, No=199 ==> ptid=X199 …) (paste0 R function)

6. Combine clinical data with diversity data (alpha.df) by patient ids (merge R function, by.x=‘ptid’, by.y=‘sample’)

7. Export new clinical data as csv file (write.csv R function) ==> submit

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1. 쉽게 배우는 R 데이터 분석 북리딩: 8장, 13장

2. Statistical Analysis of Microbiome with R: 10장

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마이크로바이옴_summary_김민규